Model comparison
Ling 2.6 Flash vs Sakana Fugu-Ultra
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Ling 2.6 Flash #154 (Estimated); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Ling 2.6 Flash and Sakana Fugu-Ultra share 2 comparable benchmark results. 2 of 8 categories are comparable. 16 results are unique to Ling 2.6 Flash; 9 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 2
- Ling 2.6 Flash only
- 16
- Sakana Fugu-Ultra only
- 9
- Comparable categories
- 2 / 8
Treat this as a split decision. Ling 2.6 Flash makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; Sakana Fugu-Ultra is the better fit if coding is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 2 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Ling 2.6 Flash and Sakana Fugu-Ultra finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Sakana Fugu-Ultra is the reasoning model in the pair, while Ling 2.6 Flash is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Sakana Fugu-Ultra gives you the larger context window at 1M, compared with 262K for Ling 2.6 Flash.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Ling 2.6 Flash | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Coding | Ling 2.6 Flash27.0 | Margin→ 37.5 | Sakana Fugu-Ultra64.5 |
| Knowledge | Ling 2.6 Flash59.0 | Margin→ 36.5 | Sakana Fugu-Ultra95.5 |
| Agentic | Ling 2.6 FlashNot measured | MarginNo overlap | Sakana Fugu-Ultra82.1 |
| Reasoning | Ling 2.6 FlashNot measured | MarginNo overlap | Sakana Fugu-Ultra93.6 |
| Multimodal | Ling 2.6 FlashNot measured | MarginNo overlap | Sakana Fugu-Ultra86.6 |
| Inst. Following | Ling 2.6 Flash57.0 | MarginNo overlap | Sakana Fugu-UltraNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 59%B 95.5%Winner: Sakana Fugu-UltraΔ 36.5GPQA: Ling 2.6 Flash scored 59%; Sakana Fugu-Ultra scored 95.5%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
SciCode
CodingA 27%B 58.7%Winner: Sakana Fugu-UltraΔ 31.7SciCode: Ling 2.6 Flash scored 27%; Sakana Fugu-Ultra scored 58.7%. Sakana Fugu-Ultra wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Ling 2.6 Flash | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Ling 2.6 FlashNot available | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Ling 2.6 Flash209.5 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Ling 2.6 Flash1.07 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Ling 2.6 Flash262K | Sakana Fugu-Ultra1M | Sakana Fugu-Ultra lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
CodingSakana Fugu-Ultra wins7 benchmarks
| Benchmark | Ling 2.6 Flash | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| SciCodeSource | 27% | 58.7% | Sakana Fugu-Ultra leads |
| AA Coding IndexSource | 25.3% | — | Not comparable |
| AA-SciCodeSource | 27.1% | — | Not comparable |
| SWE-bench ProSource | — | 73.7% | Not comparable |
| Terminal-Bench 2.0Source | — | 82.1% | Not comparable |
| LiveCodeBench v6Source | — | 93.2% | Not comparable |
| LiveCodeBench ProSource | — | 90.8% | Not comparable |
Reasoning3 benchmarks
KnowledgeSakana Fugu-Ultra wins9 benchmarks
| Benchmark | Ling 2.6 Flash | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | — | Not comparable |
| GPQASource | 59% | 95.5% | Sakana Fugu-Ultra leads |
| AA-GPQA DiamondSource | 59.3% | — | Not comparable |
| AA-HLESource | 6.2% | — | Not comparable |
| AA-Omniscience IndexSource | -65.7% | — | Not comparable |
| AA-Omniscience AccuracySource | 15.4% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 95.8% | — | Not comparable |
| GPQA-DSource | — | 95.5% | Not comparable |
| HLE w/o toolsSource | — | 50% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Ling 2.6 Flash | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| CharXivSource | — | 86.6% | Not comparable |
Frequently Asked Questions (3)
Which is better, Ling 2.6 Flash or Sakana Fugu-Ultra?
Ling 2.6 Flash and Sakana Fugu-Ultra are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, Ling 2.6 Flash or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, Ling 2.6 Flash or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for coding in this comparison, averaging 64.5 versus 27. Inside this category, SciCode is the benchmark that creates the most daylight between them.
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